University of Groningen
Towards improved risk prediction of incident atrial fibrillation and progression of atrial
fibrillation
Marcos, Ernaldo Gonsalvis
DOI:
10.33612/diss.136550017
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Towards Improved Risk Prediction of Incident Atrial Fibrillation and
Progression of Atrial Fibrillation
Ernaldo Gonsalvis Marcos
Towards improved risk prediction of incident atrial fibrillation and progression of atrial fibrillation.
Financial support by the Dutch Heart Foundation for the publication of this thesis is gratefully acknowledged.
The research described in this thesis was supported by a grant of the Dutch Heart Foundation (2010B233).
Financial support for the printing of this thesis was kindly provided by the SBOH (the employer for GP trainees and elderly care medicine trainees)
Copyright 2020, E.G.Marcos
All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by means- electronic, mechanically, by photocopying, recording or otherwiae- without express written permission from the author and, when appropriate, the publisher holding the copyrights of the published articles.
Layout and printed by: Optima Grafische Communicatie, Rotterdam, The Netherlands ISBN: 978-94-034-2550-4
Towards Improved Risk Prediction
of Incident Atrial Fibrillation and
Progression of Atrial Fibrillation
Proefschrift
ter verkrijging van de graad van doctor aan de Rijksuniversiteit Groningen
op gezag van de
rector magnificus prof. dr. C. Wijmenga en volgens besluit van het College voor Promoties.
De openbare verdediging zal plaatsvinden op woensdag 20 mei 2020 om 09.00 uur
door
Ernaldo Gonsalvis Marcos geboren op 25 maart 1983
Promotores Prof. dr. M. Rienstra Prof. dr. I.C. van Gelder Co-promotor
dr. B.A. Mulder
Beoordelingscommissie Prof. dr. A.A. Voors Prof. dr. L.V.A. Boersma Prof. dr. S.J.L. Bakker
Paranimfen Dr.B.Geelhoed Drs.M.I.H Al-Jazairi
Financial support by the following sponsors for the publication of this thesis is
gratefully acknowledged: Rijksuniversiteit Groningen, Groningen University Institute for Drug Exploration (GUIDE), Biotronik Nederland BV, Bayer Healthcare Pharmaceuticals, Biosemi BV, Pfizer BV.
Chapter 1 Introduction
Part 1- Risk markers for incident atrial fibrillation
Chapter 2 Relation of renal dysfunction with incident atrial fibrillation and
cardiovascular morbidity and mortality: The PREVEND study
Europace. 2017 Dec 1;19(12):1930-1936.
Chapter 3 Metabolomic profiling in relation to new-onset atrial fibrillation
(from the Framingham Heart Study)
Am J Cardiol. 2016 Nov 15;118(10):1493-1496.
Part 2 – Assessment of remodeling of the atrium.
Chapter 4 Increased P-wave complexity in patients with atrial fibrillation
compared to a control population.
Submitted
Part 3 – Risk markers for progression of atrial fibrillation.
Chapter 5 Atrial fibrillation progression and outcome in patients with young
onset atrial fibrillation
Europace. 2018 Nov 1;20(11):1750-1757.
Chapter 6 Atrial fibrillation progression risk factors and associated
cardio-vascular outcome in well-phenotyped patients – data from the AF-RISK study.
Europace. 2019 Dec 22 (epub ahead of print)
Chapter 7 Sex differences in atrial fibrillation progression and outcome in
patients with young onset atrial fibrillation
Int J Cardiol Heart Vasc. 2019 Nov 7;25:100429
Chapter 8 Discussion and future perspectives
Nederlandse samenvatting Dankwoord Bibliography Biography 9 23 41 55 75 93 111 123 141 145 149 151
Chapter 1
Chapter 1 11 Introduction
InTRoduCTIon
Atrial fibrillation (AF) is one of the cardiovascular epidemics of the Western world.1
Millions of Europeans suffer from AF and this number will continue to rise during the
next years, mainly due to ageing of the population and lifestyle.2,3 Not only is AF the
most frequent sustained cardiac arrhythmia, AF is not benign. AF is associated with substantial morbidity and mortality, due to an increased risk of heart failure, stroke,
decreased quality of life, cognitive dysfunction, dementia, and death.1,4-7 Therefore, AF
has an enormous impact on public health.
The pathophysiologic processes that underlie AF are complex and poorly understood. Atrial remodeling occurs as a consequence of multiple interacting mechanisms trig-gered by underlying conditions such as hypertension, heart failure, valvular disease, chronic kidney disease and/or diabetes, but also as a consequence of AF itself (AF begets
AF).8-10 Due to progressive remodeling it is challenging to maintain sinus rhythm in the
long term. Progression of AF to more sustained forms of the disease is deleterious, since it is associated with significant cardiovascular morbidity, increased hospitalizations and
mortality, among others due to heart failure, stroke, or myocardial infarction.9,11,12
Multiple interacting mechanisms, including electrical remodeling and continuous structural remodeling of the atria are thought to play a key role in the pathophysiologic
processes that set the stage for AF and AF progression.13-15 The process of remodeling
is marked by activation of renin-angiotensin-aldosterone system, cellular calcium overload, increased release of endothelin-1, heath shock proteins, natriuretic peptides, adipokines, and inflammation and oxidative stress, leading to structural remodeling as a consequence of fibrosis, cellular hypertrophy, dedifferentiation, fatty infiltration,
apoptosis and myolysis, and enlarged atria (Figure 1).5,16 Structural remodeling results in
electrical dissociation of the cardiac muscle bundles and local conduction
heterogene-ities, which helps to the initiation and perpetuation of AF.10,13,14,17,18 This
electro-anatom-ical substrate allows multiple small re-entry circuits to occur, leading to stabilization of AF. The process of remodeling is initiated by underlying conditions long before the first
episode of AF occurs (Figure 2).17 Once AF is present, the remodeling processes in the
atria progress further to constitute a vicious circle.10,16,19
Risk markers for incident atrial fibrillation
The amount of risk factors that are identified to be associated with an increased AF risk
is growing and varies over time.20 It is important to distinguish between non-modifiable
risk factors, such as advancing age, sex, and genetic and ethnic background, and modifiable risk factors, because of the possibility to interfere in these modifiable risk
12
factors creating a window for new treatment strategies (table 1).5,21, 27 Moreover, there
are important diff erences between men and women as women with AF are in general
Figure 1. Hypothetical scheme of stretch induced by hypertension, heart failure and possibly extreme endurance exercise leading to calcium overload, activation of the renin–angiotensin–aldosterone system (RAAS) and release of diff erent factors, resulting in structural remodeling and finally in AF. Reprinted with permission from de Jong et al.16
Figure 2. Time-dependent atrial remodeling and development of atrial fibrillation. Adapted from Cosio et al.17
Chapter 1 13 Introduction
older, have a higher prevalence of hypertension, valvular heart disease and HF-PEF. Modifiable risk factors include hypertension, heart failure with preserved and reduced ejection fraction, diabetes mellitus, chronic kidney disease, obesity, lifestyle including excessive alcohol consumption, smoking, caffeine consumption, drug use, psychosocial factors, air pollution, (sub)clinical hyperthyroidism, (sub)clinical vascular disease, valvular disease, cholesterol levels, length, a high normal blood pressure,
inflamma-tory diseases, and chronic kidney disease (table 1).4,22-26,28 Of these associated diseases,
among others hypertension, heart failure and obesity, cause atrial stretch, leading to calcium overload, activation of RAAS, inflammation and oxidative stress and release of
several other factors, which cause structural remodeling setting the stage for AF (Figure
1).16 Once AF eventually occurs, AF itself also increases the risk for AF to occur (Figure
2). A vicious circle is born.
Assessment of remodeling of the atrium
The severity of structural remodeling and subsequent substrate for initiation and progression of AF is time dependent and is influenced by the normal aging process as
well as underlying conditions.29 Assessing the severity of atrial remodeling is difficult
and challenging, but it is necessary to improve personalized AF therapy and ideally subsequently improve outcome. In addition, blood biomarkers may help to identify
the severity of atrial remodeling and the patients at risk for incident AF.30 Yet, limited
Table 1. Risk factors associated with development of atrial fibrillation. Adapted from Wyse et al.22
Established risk factors new and less validated risk factors
Advancing age Subclinical atherosclerosis
Male Borderline hypertension
Coronary heart disease Chronic kidney disease Hypertension (above 140/90mmHg) Subclinical hyperthyroidism Heart failure Inflammatory diseases Valvular heart disease Widened pulse pressure
Diabetes Mellitus Excessive exercise/endurance training Hyperthyroidism Excessive alcohol intake
Obstructive sleep apnea syndrome Length
Heart failure with preserved ejection fraction Increased birth weight Chronic obstructive pulmonary disease Smoking
Left atrial function/dilatation Excessive caffeine intake
Obesity Ethnicity
Atrial conduction delay/ PR interval Psychological determinants Left ventricular hypertrophy
14
data is available but AF progression has been associated with higher levels of fibrotic
and inflammatory markers, such as transforming growth factor β1.31,32 Further, through
measuring small-molecule metabolites in various biological systems, metabolomics may offer the potential for including metabolic pathways responsible for the initiation and progression of AF linking several exposures like dietary intake and the microbiota
with cardiometabolic traits.25,33,34 Quantification of atrial fibrosis may be possible with
late gadolinium enhancement magnetic resonance imaging. However, it is so far only
done by limited centers since this technique still is challenging.35-37 Therefore, clinical
and echocardiographic are predominantly used to evaluate the extent of the atrial substrate in clinical practice. For years electrocardiographic (ECG) parameters have been used to predict incident AF, with P-wave duration and PR-interval being the most
frequently studied parameters.38 A longer P wave duration predicted incident AF in
previously undiagnosed patients but unfortunately did not add predictive power over
clinical parameters associated with AF development.5, 6 The P wave duration is a
sur-rogate parameter for the total atrial activation time. Prolongation of the P wave duration and PR-interval indicate a global conduction slowing and can be assessed by measuring
the total atrial conduction time (TACT).39 This may be an additional tool to assess the
severity of atrial remodeling. TACT is measured by using 12-lead electrocardiogram and Tissue Doppler Imaging (TDI) in the 4-chamber view and defined as the interval of time from initiation of the ECG P-wave (lead II) until the peak of the local lateral left atrial Tis-sue Doppler imaging (TDI). Recent development in the noninvasive surface electrocar-diographic mapping and computers processing have made it possible to noninvasively
map atrial activation.40,41 This technique may help to assess P-wave parameters looking
at irregularities within the P-wave or at alternative lead positions, and may detect subtle regional conduction disturbances further contributing to the quantification of the
sever-ity of atrial remodeling.42-45 Another possibility to assess severity of atrial remodeling
is measuring the left atrial function.37 The contraction of the left atrium can be divided
in three functional phases: reservoir, conduit and active atrial contraction function. Those three phases of atrial contraction can be measured mechanically by strain. This is a 2-dimensional speckle tracking echocardiography that measures the deformation
of the atrial myocardium (Figure 3a).46 Further, a volumetric assessment of the atrium
can also be useful in providing information about the atrial function (Figure 3b). The
atrial volumetric assessment can be obtained from atrial volume at his maximum (just before mitral valve opens), at his minimum (when mitral valves close) and immediately before atrial contraction (before electrocardiographic P-wave). This can be used for the
Chapter 1 15 Introduction
Figure 3a. 2-dimensional speckle tracking echocardiography that measures the deformation of the atrial myocardium.
16
Risk markers for progression of atrial fibrillation.
Risk markers associated with progression of AF are less well studied but likely include comparable markers as for incident AF. The HATCH score, which stands for hyperten-sion, age >75 years, stroke or transient ischemic attack (2 points), chronic obstructive pulmonary disease and heart failure (2 points), is currently the only risk-score to predict
progression of AF.11 For example, patients with a low HATCH score of 1 already had 10%
progression rate a year (from paroxysmal to persistent AF). In validation cohorts, the HATCH score has shown only to have modest predictive value of AF progression with a
C-statistic of 0.6 and 0.62, respectively.47,48 This indicates that the current risk-stratification
for AF progression is far from optimal. This low c-statistic may be attenuated by the com-plex process of AF progression in which often subclinical cardiovascular diseases, such as borderline hypertension and subclinical coronary or peripheral artery disease will have
an influence in the course of the disease.49,50 Besides the clinical factors incorporated
in the HATCH score, different blood biomarkers are associated with atrial remodeling and could potential refine the risk scores. Inflammatory biomarkers (high-sensitivity C-reactive protein, interleukin 6, tumor necrosis alpha, adiponectin, transforming growth factor beta) and fibrotic biomarkers (procollagen, procollagen type I carboxy-terminal propeptide, I collagen telopeptide, connective tissue growth factor, matrix
metallopro-teinase-9) have been associated with atrial remodeling.31,51,52 For clinical practice, it is
interesting to predict AF progression (as longer duration of AF is associated with failure of rhythm control therapy) as this may affect the individual treatment strategy.
AIM oF ThIs ThEsIs
This thesis focuses on the risk markers for incident AF and progression of AF. In chapter
2 we assess the relation of renal dysfunction with incident AF and the association with cardiovascular morbidity and mortality by using the Prevention of Renal and Vascular
End-stage Disease (PREVEND) study. In chapter 3 we evaluate metabolic profiling in the
relation to new-onset AF in the Framingham Heart Study. In chapter 4 we continue to
search for a new technique assessing the severity of the atrial remodeling using body
surface mapping to assess p-wave complexity. In chapter 5, 6 and 7 we focus on AF
progression. In Chapter 5 we focus on the incidence and risk factors of AF progression
in a population with young onset AF. Chapter 6 the incidence and clinical and
echo-cardiographic factors and blood biomarkers associated with AF progression in patients
with a short history of AF is investigated. Chapter 7 focuses on sex differences in clinical
profile, AF progression incidence and risk factors. Finally, in chapter 8 the thesis will be
Chapter 1 17 Introduction
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11. de Vos CB, Pisters R, Nieuwlaat R, et al. Progression from paroxysmal to persistent atrial fibrilla-tion clinical correlates and prognosis. J Am Coll Cardiol. 2010;55(8):725-731.
12. Piccini JP, Hammill BG, Sinner MF, et al. Clinical course of atrial fibrillation in older adults: The importance of cardiovascular events beyond stroke. Eur Heart J. 2014;35(4):250-256.
13. Allessie MA, Konings K, Kirchhof CJ, Wijffels M. Electrophysiologic mechanisms of perpetuation of atrial fibrillation. Am J Cardiol. 1996;77(3):10A-23A.
14. Ausma J, van der Velden HM, Lenders MH, et al. Reverse structural and gap-junctional remodeling after prolonged atrial fibrillation in the goat. Circulation. 2003;107(15):2051-2058.
15. Nattel S, Guasch E, Savelieva I, et al. Early management of atrial fibrillation to prevent cardiovas-cular complications. Eur Heart J. 2014;35(22):1448-1456.
16. De Jong AM, Maass AH, Oberdorf-Maass SU, Van Veldhuisen DJ, Van Gilst WH, Van Gelder IC. Mechanisms of atrial structural changes caused by stretch occurring before and during early atrial fibrillation. Cardiovasc Res. 2011;89(4):754-765.
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17. Cosio FG, Aliot E, Botto GL, et al. Delayed rhythm control of atrial fibrillation may be a cause of failure to prevent recurrences: Reasons for change to active antiarrhythmic treatment at the time of the first detected episode. Europace. 2008;10(1):21-27.
18. Schotten U, Verheule S, Kirchhof P, Goette A. Pathophysiological mechanisms of atrial fibrillation: A translational appraisal. Physiol Rev. 2011;91(1):265-325.
19. Nattel S, Shiroshita-Takeshita A, Cardin S, Pelletier P. Mechanisms of atrial remodeling and clini-cal relevance. Curr Opin Cardiol. 2005;20(1):21-25.
20. Schnabel RB, Yin X, Gona P, et al. 50 year trends in atrial fibrillation prevalence, incidence, risk factors, and mortality in the framingham heart study: A cohort study. Lancet. 2015;386(9989):154-162.
21. Gorenek B, Pelliccia A, Benjamin EJ, et al. European heart rhythm association (EHRA)/european association of cardiovascular prevention and rehabilitation (EACPR) position paper on how to prevent atrial fibrillation endorsed by the heart rhythm society (HRS) and asia pacific heart rhythm society (APHRS). Europace. 2017;19(2):190-225.
22. Wyse DG, Van Gelder IC, Ellinor PT, et al. Lone atrial fibrillation: Does it exist? J Am Coll Cardiol. 2014;63(17):1715-1723.
23. Frost L, Hune LJ, Vestergaard P. Overweight and obesity as risk factors for atrial fibrillation or flutter: The danish diet, cancer, and health study. Am J Med. 2005;118(5):489-495.
24. Gami AS, Hodge DO, Herges RM, et al. Obstructive sleep apnea, obesity, and the risk of incident atrial fibrillation. J Am Coll Cardiol. 2007;49(5):565-571.
25. Mayr M, Yusuf S, Weir G, et al. Combined metabolomic and proteomic analysis of human atrial fibrillation. J Am Coll Cardiol. 2008;51(5):585-594.
26. Schoonderwoerd BA, Smit MD, Pen L, Van Gelder IC. New risk factors for atrial fibrillation: Causes of ‘not-so-lone atrial fibrillation’. Europace. 2008;10(6):668-673.
27. Weng LC, Preis SR, Hulme OL, et al. Genetic predisposition, clinical risk factor burden, and life-time risk of atrial fibrillation. Circulation. 2018;137(10):1027-1038.
28. Walters TE, Wick K, Tan G, et al. Psychological distress and suicidal ideation in patients with atrial fibrillation: Prevalence and response to management strategy. J Am Heart Assoc. 2018;7(18):e005502.
29. Guichard JB, Nattel S. Atrial cardiomyopathy: A useful notion in cardiac disease management or a passing fad? J Am Coll Cardiol. 2017;70(6):756-765.
30. Oral H, Pappone C, Chugh A, et al. Circumferential pulmonary-vein ablation for chronic atrial fibrillation. N Engl J Med. 2006;354(1533-4406; 0028-4793; 9):934-941.
31. Smit MD, Maass AH, De Jong AM, Muller Kobold AC, Van Veldhuisen DJ, Van Gelder IC. Role of inflammation in early atrial fibrillation recurrence. Europace. 2012;14(6):810-817.
32. Akutsu Y, Kaneko K, Kodama Y, et al. A combination of P wave electrocardiography and plasma brain natriuretic peptide level for predicting the progression to persistent atrial fibrillation: Com-parisons of sympathetic activity and left atrial size. J Interv Card Electrophysiol. 2013;38(2):79-84. 33. De Souza AI, Cardin S, Wait R, et al. Proteomic and metabolomic analysis of atrial profibrillatory
remodelling in congestive heart failure. J Mol Cell Cardiol. 2010;49(5):851-863.
34. Alonso A, Krijthe BP, Aspelund T, et al. Simple risk model predicts incidence of atrial fibrillation in a racially and geographically diverse population: The CHARGE-AF consortium. J Am Heart Assoc. 2013;2(2):e000102.
35. Habibi M, Lima JA, Khurram IM, et al. Association of left atrial function and left atrial enhancement in patients with atrial fibrillation: Cardiac magnetic resonance study. Circ Cardiovasc Imaging. 2015;8(2):e002769.
Chapter 1 19 Introduction
36. Oakes RS, Badger TJ, Kholmovski EG, et al. Detection and quantification of left atrial structural remodeling with delayed-enhancement magnetic resonance imaging in patients with atrial fibril-lation. Circufibril-lation. 2009;119(13):1758-1767.
37. Kuppahally SS, Akoum N, Burgon NS, et al. Left atrial strain and strain rate in patients with parox-ysmal and persistent atrial fibrillation: Relationship to left atrial structural remodeling detected by delayed-enhancement MRI. Circ Cardiovasc Imaging. 2010;3(3):231-239.
38. Magnani JW, Johnson VM, Sullivan LM, et al. P wave duration and risk of longitudinal atrial fibrilla-tion in persons >/= 60 years old (from the framingham heart study). Am J Cardiol. 2011;107(6):917-921.e1.
39. De Vos CB, Weijs B, Crijns HJ, et al. Atrial tissue doppler imaging for prediction of new-onset atrial fibrillation. Heart. 2009;95(10):835-840.
40. Haissaguerre M, Hocini M, Shah AJ, et al. Noninvasive panoramic mapping of human atrial fibril-lation mechanisms: A feasibility report. J Cardiovasc Electrophysiol. 2013;24(6):711-717. 41. Guillem MS, Climent AM, Castells F, et al. Noninvasive mapping of human atrial fibrillation. J
Cardiovasc Electrophysiol. 2009;20(5):507-513.
42. Rangel MO, O’Neal WT, Soliman EZ. Usefulness of the electrocardiographic P-wave axis as a predictor of atrial fibrillation. Am J Cardiol. 2016;117(1):100-104.
43. Nielsen JB, Kuhl JT, Pietersen A, et al. P-wave duration and the risk of atrial fibrillation: Results from the copenhagen ECG study. Heart Rhythm. 2015;12(9):1887-1895.
44. Cheng S, Keyes MJ, Larson MG, et al. Long-term outcomes in individuals with prolonged PR interval or first-degree atrioventricular block. JAMA. 2009;301(24):2571-2577.
45. Holmqvist F, Platonov PG, Carlson J, Zareba W, Moss AJ, MADIT II Investigators. Altered interatrial conduction detected in MADIT II patients bound to develop atrial fibrillation. Ann Noninvasive Electrocardiol. 2009;14(3):268-275.
46. Hoit BD. Left atrial size and function: Role in prognosis. J Am Coll Cardiol. 2014;63(6):493-505. 47. Potpara TS, Stankovic GR, Beleslin BD, et al. A 12-year follow-up study of patients with newly
diagnosed lone atrial fibrillation: Implications of arrhythmia progression on prognosis: The belgrade atrial fibrillation study. Chest. 2012;141(2):339-347.
48. Barrett TW, Self WH, Wasserman BS, McNaughton CD, Darbar D. Evaluating the HATCH score for predicting progression to sustained atrial fibrillation in ED patients with new atrial fibrillation. Am J Emerg Med. 2013;31(5):792-797.
49. Conen D, Tedrow UB, Koplan BA, Glynn RJ, Buring JE, Albert CM. Influence of systolic and diastolic blood pressure on the risk of incident atrial fibrillation in women. Circulation. 2009;119(16):2146-2152.
50. Weijs B, Pisters R, Haest RJ, et al. Patients originally diagnosed with idiopathic atrial fibrillation more often suffer from insidious coronary artery disease compared to healthy sinus rhythm controls. Heart Rhythm. 2012;9(12):1923-1929.
51. Rienstra M, Sun JX, Magnani JW, et al. White blood cell count and risk of incident atrial fibrillation (from the framingham heart study). Am J Cardiol. 2012;109(4):533-537.
52. Rosenberg MA, Maziarz M, Tan AY, et al. Circulating fibrosis biomarkers and risk of atrial fibrilla-tion: The cardiovascular health study (CHS). Am Heart J. 2014;167(5):723-8.e2.
Part 1
Chapter 2
Relation of renal dysfunction with incident atrial
fibrillation and cardiovascular morbidity and
mortality: The PREVEnd study.
Ernaldo G. Marcos, MD Bastiaan Geelhoed, PhD Pim Van Der Harst, MD, PhD Stefan J.L. Bakker, MD, PhD Ron T. Gansevoort, MD, PhD Hans L. Hillege, MD, PhD Isabelle C. Van Gelder, MD, PhD Michiel Rienstra, MD, PhD
24 Part 1
Risk markers for incident atrial fibrillation
ABsTRACT And KEywoRds
Aims. Renal dysfunction is a risk factor for cardiovascular disease, including atrial fibrillation (AF) and mortality. However, the exact pathobiology linking different renal dysfunction measures, such as albumin excretion or glomerular filtration rate (GFR), to cardiovascular- and AF risk are unclear. In this study we investigated the association of several renal function measures and incident AF, and whether the relation between renal measures and outcomes is modified by AF.
Methods. We examined 8,265 individuals (age 49±13 years, 50% women) included in the PREVEND study. We used albumin excretion (morning void and 24-hours urine samples), serum creatinine, cystatin C, and Cystatin C-based, based, and creatinine-cystatin C-based GFR as renal function measures.
Results. During a follow-up of 9.8±2.3 years, 267 participants (3.2%) developed AF. In the multivariate-adjusted model, GFR, estimated by creatinine, cystatin C, or the com-bination was not associated with incident AF. However, increased albumin excretion was strongly associated with incident AF; urine albumin concentration and excretion (HRmorning void 1.10, P=0.005, and HR24-hr collection 1.05, P=0.033) and albumin creatinine ratio
(HRmorning void 1.05, P=0.010, and HR24-hr collection 1.06, P<0.001). Interaction-terms of incident
AF and renal measures were not significant for incident cerebrovascular events, periph-eral vascular events, ischemic heart disease, heart failure and mortality.
Conclusion. In this community-based cohort, increased albumin excretion, and not GFR, was associated with incident AF, independent of established cardiovascular risk factors. Incidence of AF did not largely alter the association of renal dysfunction and cardiovascular outcomes.
Chapter 2 Renal measures and incident AF
CondEnsEd ABsTRACT
In 8,265 individuals from the community-based PREVEND study, albuminuria was asso-ciated with incident AF, independent of established cardiovascular risk factors. The as-sociation between albumin excretion and incident AF was not found for GFR. Incidence of AF did not significantly alter the association of renal dysfunction and cardiovascular outcomes.
26 Part 1
Risk markers for incident atrial fibrillation
what’s new
• Albuminuria, a measure of renal dysfunction, but not GFR, is related to incidence of AF, independent from other cardiovascular risk factors in the general population. • Albuminuria can be measured in first morning void or 24-hour urine collection, the
association with incident AF was comparable for both methods.
• Albumin excretion can be used in those with and without incident AF to predict cardiovascular events, since the association of renal measures and incident cere-brovascular events, peripheral vascular events, ischemic heart disease, heart failure, and all-cause mortality, was largely similar in those with and without incident AF.
Chapter 2 Renal measures and incident AF
InTRoduCTIon
Atrial fibrillation (AF) is a common cardiac arrhythmia and is a major public health
prob-lem with an incidence of 1 in 4 for persons over the age of 55 years in the Netherlands.1
Individuals with AF are at increased risk for cardiovascular complications such as stroke,
dementia, heart failure, and death.2, 3 Known risk factors and conditions associated with
AF are advancing age, male sex, diabetes mellitus, hypertension, valve disease,
myo-cardial infarction, heart failure, and obesity.2 More recently, renal dysfunction has also
been related to incident AF.4, 5 Renal dysfunction is casually related to hypertension, left
ventricular hypertrophy, inflammation, hypercoagulability, and may activate the
renin-angiotensin-aldosterone system.6 All these mechanisms also increase the susceptibility
of the development of AF. Thus far, no studies have studied different measures of renal function such as serum creatinine or cystatin C, estimated glomerular filtration rates, and albumin excretion, in relation to development of AF.
There is abundant data that reduced renal function is associated with increased
cardio-vascular events, in both populations with and without prevalent AF.7, 8 Also in prevalent
AF populations, renal dysfunction has been associated with increased stroke risk.9
Whether the relation of renal function and outcomes is modified by AF has not been investigated. It has been postulated that in the setting of AF, the associated potential de-creased cardiac output, potential electrolyte disturbances, changed pharmacokinetics
of medication used in AF, or associated comorbidities,10 may influence renal measures
in AF, and potentially its relation with cardiovascular outcome.
Although the exact pathobiology linking renal dysfunction to cardiovascular and AF risk are unknown; both albumin excretion and glomerular filtration rates were
dem-onstrated independent markers of cardiovascular risk.11 Albumin excretion is mainly a
consequence of glomerulus damage, and is considered a marker of systemic vascular damage or microvascular disease. The above led to the idea that there may be differ-ences between renal measures and the association with AF, and that the magnitude of effect of renal measures and cardiovascular outcome may be influenced by AF.
METhods
Population. The PREVEND was founded in 1997, and includes a cohort of 8,592 individu-als, enriched with individuals with a urinary albumin excretion >10ml/L in their morning void, since the purpose study was to investigate the natural course of increased levels
28 Part 1
Risk markers for incident atrial fibrillation
individuals with an urinary albumin excretion >10mg/L and 3,395 individuals with an urinary albumin excretion <10mg/L were invited to the PREVEND outpatient clinic. The final cohort consisted of 8,592 individuals. At the baseline visit, in addition to detailed information about demographics, health behaviours, anthropometric measurements, cardiovascular and metabolic risk factors, also blood samples and two 24-hour urine samples on 2 consecutive days were collected. For present analysis, we excluded par-ticipants without any ECG (n=248), and those with prevalent AF (n=79), leaving 8,265 individuals for analysis. Characteristics and outcomes of the excluded individuals were comparable to the included population. The PREVEND study was approved by the insti-tutional medical Ethics Committee and conducted in accordance with the Declaration of Helsinki. All individuals provided written informed consent.
Follow up. The follow-up duration was calculated as the time between the baseline visit to the last contact date, death, or December 31, 2008, whichever came first.
Covariate definitions. Systolic and diastolic blood pressures were measured by using an automatic Dinamap XL Model 9300 series device, and were calculated as the mean of the last two measurements of the two visits. Hypertension was defined as systolic blood pressure ≥140 mmHg, diastolic blood pressure ≥90 mmHg, or self-reported use of anti-hypertensive medication. The ratio of weight to height squared (kg/m2) was used for
calculation of body mass index (BMI). Obesity was defined as a BMI >30 kg/m2. Diabetes
mellitus was defined as a fasting plasma glucose >7.0 mmol/L (126 mg/dL), a nonfasting plasma glucose >11.1 mmol/L, or use of anti-diabetic medication. Hypercholesterolemia was defined as total serum cholesterol above 6.5 mmol/l (251 mg/dl), or serum choles-terol above 5.0 mmol/l (193 mg/dl) in those with previous myocardial infarction or when lipid-lowering medication was used. Smoking was defined as using nicotine within the previous year. Alcohol consumption was defined as 2 or more alcoholic drinks per week. Previous myocardial infarction or stroke was defined as hospitalization for myocardial infarction or stroke for at least 3 days. A committee of heart failure experts adjudicated
all individuals with heart failure at baseline according to previously published criteria.12
Peripheral artery disease was defined as an ankle-brachial index <0.9.
Renal measures. Serum cystatin C were determined by nephelometry (BNII, Dade Behring Diagnostics, Marburg, Germany). Intra- and interassay coefficients of variation were <4.1% and 3.3% for cystatin C. Serum and urine creatinine were determined by Kodak Ektachem dry chemistry (Eastman Kodak, Rochester, New York, USA) using an automated enzymatic method. The intra- and interassay coefficients of variation of
serum creatinine were 0.9% and 1.1%.13 The intra- and interassay coefficients of
Chapter 2 Renal measures and incident AF
GFR based on serum concentration of creatinine (eGFR-creatinine) was estimated using the Simplified Modification of Diet in Renal Disease formula (MDRD). The GFR based on serum concentration of cystatin C (eGFR-cystatin C) was estimated with the Chronic Kid-ney Disease (CKD) Epidemiology Collaboration equation for cystatin C. The GFR based on serum concentrations of both creatinine and cystatin C (eGFR-creatinine-cystatin C) was estimated using the Epidemiology Collaboration equation for creatinine-cystatin C. Urinary albumin was determined by a commercial immunoturbidimetry assay with a sensitivity of 2.3 mg/L and interassay and intra-assay coefficients of variation of 4.4% and 4.3%, respectively (BNII, Dade Behring Diagnostics, Marburg, Germany). Urine albumin concentration was measured in the first morning void. Urine albumin excretion was measured in two consecutive 24-hour urine collections, and the average value was calculated. Urine albumin-creatinine ratio was calculated based on both the first morn-ing void and 24-hour urine collections.
Atrial Fibrillation and cardiovascular events during follow up. Incident AF
ascertain-ment has been described in detail previously.2 Briefly, incident AF was diagnosed if
either atrial flutter or AF was present on a 12-lead ECG obtained at 1 of the 3 PREVEND follow-up visits or at an outpatient visit or hospital admission in the 2 hospitals in the city of Groningen. For the date of incident AF, the date of the first ECG with a definite diagnosis of AF or atrial flutter was used. Information on cardiovascular events was obtained from PRISMANT, the Dutch national registry of hospital discharge diagnoses. Ischemic heart disease consisted of acute myocardial infarction [ICD code 410], acute and subacute ischemic heart disease [ICD 411], coronary artery bypass grafting or percu-taneous transluminal coronary angioplasty, cerebrovascular events consisted of occlu-sion or stenosis of the precerebral (ICD 433) or cerebral arteries (ICD 434), subarachnoid haemorrhage (ICD 430), and peripheral vascular events consisted of other vascular interventions such as percutaneous transluminal angioplasty or bypass grafting of aorta and peripheral vessels. A committee of heart failure experts adjudicated all heart failure events according to previously published criteria. Data on mortality were obtained through the municipal registration.
statistical analysis. A statistical weighting method was used in the prespecified Cox proportional-hazards regression analyses, to adjust the overselection of individuals with microalbuminuria at baseline, and allow generalization of results to the general population. In the weighted Cox regressions, people with urinary albumin excretion <10 mg/l had a weighing factor of 11.92 and people with urinary albumin excretion >10 mg/l had a weighing factor of 1.66. The numbers 11.92 and 1.66 were selected based on the unequal inclusion probabilities.
30 Part 1
Risk markers for incident atrial fibrillation
Individual characteristics were presented as mean ± standard deviation or median (range) for continuous variables and counts with percentages for categorical variables. We performed 3 prespecified Cox proportional-hazards regression models to relate the renal measures to incident AF. Model 1 were univariate analyses, in Model 2, we adjusted for established AF risk factors (age, sex, BMI, antihypertensive treatment, previous stroke, heart failure, previous myocardial infarction, diabetes, peripheral artery disease,
smoking, PR-interval duration, NT-proBNP).15 In Model 3 we adjusted for all covariates
included in Model 2, plus interim myocardial infarction and heart failure, occurring after baseline before incident AF. The proportionality assumption was checked by calculat-ing the Schoenfeld residuals, and where needed time-varycalculat-ing covariates were included to avoid proportionality violations. We used Cox time-dependent regression analyses, to study whether the association of renal measures and cardiovascular outcome, is modified by AF, by including interaction terms of renal measures and AF as time-varying covariate. We adjusted for age, sex, heart failure, antihypertensive drug use, diabetes, previous stroke, previous myocardial infarction, peripheral artery disease, N-terminal prohormone of brain natriuretic peptide (NT-proBNP). All analyses were performed
us-ing R package (version 3.03), and a p-value <0.05 was considered statistically significant.
REsulTs
Individual characteristics. The study sample consisted of 8,265 individuals with mean age of 49±13 years, half of them were women. Individual characteristics are shown in Table 1. In total, 466 individuals (5.7%) had an estimated creatinine-based GFR<60 ml/ min/1.73m², and 1762 (21.3%) had albuminuria (urinary albumin concentration ≥20 mg/L).
Renal measures and incident atrial fibrillation. Total follow-up duration was 81,018 person-years. During a mean follow-up of 9.8 years, 267 (3.2%) individuals developed incident AF. None of the GFR measures was associated with incident AF, with no differ-ences between the GFR based on creatinine, cystatin C or combined method. Albumin
excretion was strongly associated with incident AF (Table 2). Higher urine albumin
con-centration and albumin creatinine ratio, measured in first morning void samples, were associated with an increased risk of incident AF (multivariable-adjusted hazard ratio was 1.12 [95% confidence interval (CI) 1.04-1.20] and 1.05 [95% CI 1.00-1.11], respec-tively). The association remained unchanged after adjustment for interim heart failure or myocardial infarction occurring after baseline but before incident AF. A Kaplan Meier curve for three groups based on urine albumin concentration (<20, 20-200, and >200
Chapter 2 Renal measures and incident AF
albumin excretion and albumin creatinine ratio, determined from 24-hour urine collec-tions, were associated with an increased risk of incident AF, also after adjustment for interim heart failure or myocardial infarction (multivariable-adjusted hazard ratio was 1.07 [95% CI 1.02-1.11] for urine albumin concentration, and 1.07 [95% CI 1.04-1.10] for albumin creatinine ratio). The association remained also unchanged after adjustment for interim heart failure or myocardial infarction.
Table 1. Individual characteristics.
Clinical characteristics Total population (n=8265)
Age (years) 49±13
Male sex 4120 (49.8%)
Caucasian 7844 (94.9%)
Smoked 3670 (44.7%)
Alcohol consumption 4873 (59.3%) Diastolic blood pressure (mmHg) 74±10 Systolic blood pressure (mmHg) 129±20 Peripheral artery disease 291 (3.7%)
BMI (kg/m²) 26±4 Antihypertensive therapy 1098 (16.1%) NT-proBNP (ng/L) 37 (17-73) High sensitive CRP (mg/L) 1.3 (0.6-2.9) Previous stroke 57 (0.7%) PR-interval duration (ms) 158 (143-172) Diabetes 310 (3.8%) Heart rate (bpm) 69±10 Hypercholesterolemia 361 (4.6%) Hypertension 2237 (27.8%)
Previous myocardial infarction 251 (3.1%)
Heart failure 18 (0.2%)
Renal measures
Serum creatinine (umol/L) 82 (74-92) Serum cystatin C (mg/dL) 0.77 (0.69-0.87) eGFR creatinine-based(ml/min/1.73m²) 80 (71-90) eGFR cystatin- C (ml/min/1.73m²) 100 (85-118) eGFR creatinine-cystatin-C(ml/min/1.73m²) 91 (80-104) Urinary albumin concentration(mg/L) 6.9 (4.16-13.03) Urinary albumin excretion(mg/24hrs) 9.4 (6.3-17.63) Albumin creatinine ratio(mg/g) 7.0 (4.8-13.0)
Abbreviations: BMI=body mass index; CRP=C-reactive protein; eGFR=estimated glomerular filtration rate; NT-proBNP=N-terminal prohormone of brain natriuretic peptide
32 Part 1
Risk markers for incident atrial fibrillation
Table 2. Associa tion of r enal func tion me asur e and incident AF . Variable Model 1 Model 2 Model 3 h R (95% CI) P-value h R (95% CI) P-value h R (95% CI) P-value Cr ea
tinine (per 10umol/L)
1.07(1.05-1.09) <0.001 0.88(0.76-1.02) 0.092 0.90(0.78-1.04) 0.155 Cys ta tin C (per 0.3mg /dL) 1.49(1.38-1.61) <0.001 0.99(0.80-1.23) 0.945 0.98(0.76-1.26) 0.852 eGFR cr ea
tinine (per 15ml/min/1.73m²)
0.73(0.62-0.86) <0.001 1.21(1.00-1.48) 0.052 1.19(0.97-1.45) 0.096 eGFR c ys ta
tin C (per 15ml/min/1.73m²)
0.70(0.64-0.77) <0.001 0.95(0.87-1.03) 0.216 0.96(0.88-1.05) 0.350 eGFR cr ea tinine -c ys ta
tin C (per 15ml/min/1.73m²)
0.60(0.53-0.68) <0.001 1.01(0.93-1.09) 0.886 1.01(0.92-1.10) 0.897 Urine albumin c onc entr ation (per 100mg /l) in fir st -morning v oid sample 1.19(1.15-1.23) <0.001 1.12(1.04-1.20) 0.002 1.10(1.03-1.17) 0.005 Albumin cr ea tinine r atio (per 100mg /g ) in fir st -morning v oid sample 1.10(1.07-1.12) <0.001 1.05(1.00-1.11) 0.038 1.05(1.00-1.10) 0.033 Urine albumin e xcr etion (per 100mg /24 hr s) in 24-hr s urine c ollec tion 1.10(1.08-1.12) <0.001 1.07(1.02-1.11) 0.003 1.05(1.01-1.09) 0.010 Albumin cr ea tinine r atio (per 100mg /g ) in 24-hr s urine c ollec tion 1.09(1.07-1.10) <0.001 1.07(1.04-1.10) <0.001 1.06(1.02-1.09) <0.001 Model 1: Unadjus ted. Model 2: Adjus ted for ag e, se x, BMI, antihypert ensiv e tr ea tment , pr evious str ok e, he art failur e, pr evious my oc ar dial inf ar ction, diabe tes, peripher al art er y dise ase, smok -ing, PR -int er val dur ation, NT -pr oBNP . Model 3: Adjus ted for ag e, se x, BMI, antihypert ensiv e tr ea tment , he art failur e, pr evious my oc ar dial inf ar ction, diabe tes, peripher al art er y dise ase, smoking, PR -int er val dur ation, NT -pr oBNP , int erim my oc ar dial inf ar ction, int erim he art f ailur e. Abbr evia tions: BMI=body mass inde x; CI=c onfidenc e int er val; eGFR=es tima ted glomerular filtr ation ra te; HR=haz ar d ra tio; NT -pr oBNP=N-terminal pr ohormone of br ain na triur etic pep tide.
Chapter 2 Renal measures and incident AF
Relation between renal measures and cardiovascular outcome in atrial fibrillation. We studied whether the association of renal function and cardiovascular outcome is diff erent in individuals with incident AF versus those without incident AF. We included interaction terms of each renal measure and incident AF as time-varying covariate, into the regression model. Except for cystatin C, there were no significant interaction terms between renal function measures and outcome, implying no diff erent relation between
renal measure and cardiovascular outcome (Table 3). There was one significant
inter-action between cystatin C and incident AF for the association with the combination of cerebrovascular events, peripheral vascular events, ischemic heart disease (hazard ratio 0.72 [95% CI 0.57-0.91], p=0.007). The hazard ratio of cystatin C to predict the combina-tion of cerebrovascular events, peripheral vascular events, ischemic heart disease is lower in individuals with incident AF compared to those without AF.
Figure 1. Kaplan-Meier estimates of the cumulative incidence of AF, according to three groups of urine albumin concentration (<20, 20-200, and >200 mg/L).
34 Part 1
Risk markers for incident atrial fibrillation
Table 3. Associa tion of r enal func tion me asur e and incident c ar dio vascular e vent s, he art f ailur e and de ath, modified b y time -v ar ying AF . 1 Int er ac tion t erm o f r enal me asur e and time -v ar ying AF Combina tion o f cer ebr ov ascular e vent s, peripher al v ascular ev ent s, ischemic he art dise ase h eart f ailur e 3 de ath Multiv aria te h R 2 (95% CI) P-value Multiv aria te h R 2 (95% CI) P-value Multiv aria te h R 2 (95% CI) P-value Cr ea
tinine (per 0.05unit
s)*AF 0.77(0.52-1.13) 0.186 1.42(0.87-2.32) 0.162 1.08(0.83-1.40) 0.578 Cys ta
tin C (per 0.1unit
s)*AF 0.72(0.57-0.91) 0.007 0.74(0.37-1.46) 0.386 0.78(0.56-1.08) 0.128 eGFR cr ea
tinine (per 0.1unit
s)*AF 1.41(0.62-3.23) 0.415 0.57(0.29-1.11) 0.097 0.80(0.50-1.28) 0.342 eGFR c ys ta
tin C (per 0.1unit
s)*AF 1.15(0.73-1.80) 0.548 1.01(0.62-1.65) 0.967 1.27(0.90-1.78) 0.178 eGFR cr ea tinine -c ys ta
tin C (per 0.1unit
s)*AF 1.40(0.65-3.00) 0.389 0.66(0.33-1.33) 0.246 1.06(0.61-1.84) 0.826 Urine albumin c onc entr ation in fir st -morning v
oid sample (per 1.0 unit
s)*AF 1.24(0.63-2.43) 0.531 0.80(0.27-2.42) 0.695 0.69(0.31-1.53) 0.361 Albumin cr ea tinine r atio in fir st -morning v
oid sample (per 1.0 unit
s)*AF 1.28(0.64-2.57) 0.486 1.58(0.62-4.07) 0.340 0.97(0.39-2.41) 0.953 Urine albumin e xcr etion in 24-hr s urine c ollec
tion (per 0.5 unit
s)* AF 1.20(0.89-1.63) 0.240 0.86(0.51-1.44) 0.569 0.91(0.65-1.28) 0.585 Albumin cr ea tinine r atio in 24-hr s urine c ollec
tion (per 1.0 unit
s)*AF 1.56(0.87-2.81) 0.139 0.69(0.22-2.15) 0.518 0.77(0.39-1.54) 0.466 No signific ant int er ac tion me ans no diff er enc e in the associa tion be tw een renal func tion me asur e and car dio vascular out come for the AF ver sus no AF gr oup . A signifi -cant int er ac tion me ans tha t the associa tion be tw een renal func tion and car dio vascular out come is diff er ent for those with AF ver sus no AF . When the haz ar d ra tio of the int er ac tion-term is gr ea ter than 1, the associa tion be tw een the renal func tion me asur e and car dio vascular ev ent str ong er for the AF gr oup than it is for the no AF gr oup . 1All r enal func tion me asur es w er e log arithmic ally tr ansf ormed and c ent er ed ar ound their me ans. 2 Adjus ted for se x, ag e, AF , NT -pr oBNP , antihypert ensiv e drug use, diabe tes, peripher al art er y dise ase, pr evious my oc ar dial inf ar ction, pr ev alent he art failur e, pr evious str ok e and the r enal func tion me asur e v
ariable (as included in the int
er ac tion-term) it self .
3 In the analysis with out
come he art f ailur e, individuals with pr ev alent HF w er e e xcluded. Abbr evia tions: AF=a trial fibrilla tion; CI=c onfidenc e int er val; eGFR=es tima ted glomerular filtr ation ra te; HR=haz ar d ra tio; NT -pr oBNP=N-terminal pr ohormone of br ain na triur etic pep tide
Chapter 2 Renal measures and incident AF
dIsCussIon
In present community-based cohort we found that albumin excretion was associated with incident AF, and not plasma markers of renal function or GFR. These associations were independent of established cardiovascular risk factors, and not mediated via the development of heart failure or myocardial infarction during follow up. Furthermore, the association of renal measures and incident cerebrovascular events, peripheral vascular events, ischemic heart disease, heart failure and all-cause mortality, was largely similar in those with and without incident AF.
Renal dysfunction and incident AF. We used both first morning void samples and 24-hour urine collections, and albumin excretion was predictive for AF, independent of the sampling method. The relation between renal dysfunction and risk of AF has been estab-lished in several cohorts, albeit that not all studies found an association. This may be the result of different populations studied, and different measures of renal function used. Especially in high-risk populations such as coronary heart disease, or hypertension; loss of GFR, measured mainly using creatinine, but also cystatin C has been used, was
associated with prevalent AF.16 In longitudinal, community-based cohorts the relation
between GFR and incident AF was less prominent. In elderly included in the
Cardiovas-cular Health Study no relation was found.17 In the Atherosclerosis Risk in Communities
(ARIC) study and Reasons for Geographic and Racial Differences in Stroke (REGARDS)
study, however, a relation between GFR and incident AF was found.4, 5 Those studies,
and others also found a positive relation between albumin excretion measured in the
first morning void sample and incident AF.4, 5 From prior studies it is known that GFR is
especially predictive in populations with chronic kidney disease,18 and less predictive
in the general population with predominantly healthy individuals with normal renal function. In the general population, however, albumin excretion is more predictive than
GFR of future cardiovascular events.19 So, both albumin excretion and GFR are markers
of renal dysfunction, and as recently demonstrated in a large meta-analysis, both have
additional value when predicting future cardiovascular events.11 More mechanistically,
albumin excretion is considered a marker of systemic vascular damage or microvascular
disease, where GFR is more a marker of kidney disease.11 This may explain our divergent
findings on albumin excretion and GFR in our community-based cohort. The mechanisms underlying the association between renal dysfunction and incident AF, may relate to the association of renal dysfunction, and especially albumin excretion, and endothelial
dys-function and hypertension, and is considered a marker of generalized vascular disease.6
Both clinical and subclinical vascular disease are associated with incident AF. Also, renal dysfunction is associated with inflammation and hypercoagulability, and both are a known pathophysiological mechanisms involved in development and progression of
36 Part 1
Risk markers for incident atrial fibrillation
AF.20 Furthermore, renal dysfunction may activate the renin-angiotensin-aldosterone
system and lead to salt and water retention, causing left ventricular hypertrophy, and subsequent diastolic dysfunction with volume overload of the atria, which in turn may lead to AF by atrial dilatation. More studies are needed to determine whether there is a direct causal link between renal dysfunction and incident AF.
Renal dysfunction and cardiovascular outcomes. Several studies have shown that renal disease measured by a decrease in GFR or increase in albumin excretion are associ-ated with increased risk of death and cardiovascular events in patients with coronary
artery disease and in general population.8 There are also studies performed solely in
patients with AF. In those studies renal dysfunction is associated with an increased risk
of stroke.9 However, it is unknown whether effect modification by incident AF is present.
We found no significant interaction between each renal measures and AF when studying the combination of cerebrovascular events, peripheral vascular events, ischemic heart disease, heart failure and mortality, with one exception. So, we found no robust evi-dence that the relation between renal measures and cardiovascular outcome is different when AF occurs. Therefore, we cannot confirm the postulation by Boriani et al. that in AF, the decreased cardiac output, electrolyte disturbances, changed
pharmacokinet-ics, or associated comorbidities,10 may influence the relation between renal measures
and cardiovascular outcome in AF. This may imply that specific risk prediction models including renal measures for populations with and without AF are not necessary. strengths and limitations. Strengths of our study are the large and contemporary community-based cohort, with a detailed clinical and renal assessment and a strong validation of incident AF and cardiovascular events. We had in PREVEND a wide range of renal measures available; albumin excretion (morning void and 24-hours urine samples), serum creatinine, cystatin C, and Cystatin C-based, creatinine-based, and creatinine-cystatin C-based GFR.
Most limitations are the result of the observational design of the community-based cohort study. Despite the statistical weighting method to adjust for overselection of individuals with microalbuminuria at inclusion, our sample may not be completely similar to a randomly selected population cohort, and comparisons with other cohorts should be made carefully. We may have not captured all asymptomatic paroxysmal AF episodes because we did not have continuous ECG recordings. Further, treating physi-cians were informed about the presence of AF or other undiagnosed cardiovascular diseases, treatment was left to the discretion of the physician. We did not have detailed information about the AF-related therapies. Data on obstructive sleep apnea, and val-vular disease were widely captured in our cohort. Since the majority of patients were of
Chapter 2 Renal measures and incident AF
European ancestry, and had no or only mildly reduced renal dysfunction, results cannot be extrapolated to individuals with more impaired renal function, nor to other races and ethnicities. Furthermore, the number of individuals with incident AF was modest, which reduced our statistical power to detect significant interactions in the secondary analyses.
Conclusion. In this community-based cohort, increased albumin excretion, and not GFR, was associated with incident AF, independent of established cardiovascular risk factors. Presence of AF did not largely alter the association of renal dysfunction and cardiovascular outcomes.
Funding. The PREVEND study was supported by the Dutch Kidney Foundation (grant E0.13) and the Netherlands Heart Foundation (grant NHS2010B280). Dr. Rienstra is sup-ported by a grant from the Netherlands Organization for Scientific Research (Veni grant 016.136.055).
38 Part 1
Risk markers for incident atrial fibrillation
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Chapter 2 Renal measures and incident AF
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Chapter 3
Metabolomic profiling in relation to new-onset atrial
fibrillation (from the Framingham heart study)
Darae Ko, MD† Eric M. Riles, MD, MPH† Ernaldo G. Marcos, MD† Jared W. Magnani, MD, MSc Steven A. Lubitz, MD, MPH Honghuang Lin, PhD Michelle T. Long, MD Renate B. Schnabel, MD, MSc David D. McManus, MD Patrick T. Ellinor, MD, PhD Vasan S. Ramachandran, MD Thomas J. Wang, MD Robert E. Gerszten, MD Emelia J. Benjamin, MD, ScM Xiaoyan Yin, PhD* Michiel Rienstra, MD, PhD*
† Authors contributed equally to the manuscript.
42 Part 1
Risk markers for incident atrial fibrillation
ABsTRACT
Previous studies have shown several metabolic biomarkers to be associated with prevalent and incident atrial fibrillation (AF), but the results have not been replicated. We investigated metabolite profiles of 2,458 European ancestry participants from the Framingham Heart Study without AF at the index exam and followed them for 10 years for new-onset AF. Amino acids, organic acids, lipids, and other plasma metabolites were profiled by liquid chromatography-tandem mass spectrometry using fasting plasma samples. We conducted Cox proportional hazard analyses for association between metabolites and new-onset AF. We performed hypothesis generating analysis to identify novel metabolites and hypothesis testing analysis to confirm the previously reported associations between metabolites and AF. Mean age was 55.1±9.9 years, and 53% were women. Incident AF developed in 156 participants (6.3%) in 10 years of follow-up. A total of 217 metabolites were examined, consisting of 54 positively charged metabolites, 59 negatively charged metabolites, and 104 lipids. None of the 217 metabolites met our a
priori specified Bonferroni corrected level of significance in the multivariable analyses.
We were unable replicate previous results demonstrating associations between metabo-lites that we had measured and AF. In conclusion, in our metabolomics approach, none of the metabolites we tested were significantly associated with the risk of future AF. Keywords: Atrial fibrillation; Risk Factor; Metabolomics, Epidemiology
